The present disclosure relates to systems and methods that use semantic relations within text, and more specifically, to systems and methods that automatically detect semantic relations in textural material.
Detecting semantic relations in text is very useful in both information retrieval and question answering because it enables knowledge bases to be leveraged to score passages and retrieve candidate answers. To extract semantic relations from text, three types of approaches have been applied. Rule-based methods employ a number of linguistic rules to capture relation patterns. Feature based methods transform relation instances into a large amount of linguistic features like lexical, syntactic and semantic features, and capture the similarity between these feature vectors. Recent results mainly rely on kernel-based approaches. Many of them focus on using tree kernels to learn parse tree structure related features. Other researchers study how different approaches can be combined to improve the extraction performance. For example, by combining tree kernels and convolution string kernels, achieved the state of the art performance on ACE, which is a benchmark dataset for relation extraction.